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ICSE 2020

HyDiff: hybrid differential software analysis

Yannic Noller, Corina S. Pasareanu, Marcel Böhme, Youcheng Sun, Hoang Lam Nguyen, Lars Grunske

Full textDOI 10.1145/3377811.3380363dblp

Abstract

Detecting regression bugs in software evolution, analyzing side-channels in programs and evaluating robustness in deep neural networks (DNNs) can all be seen as instances of differential software analysis, where the goal is to generate diverging executions of program paths. Two executions are said to be diverging if the observable program behavior differs, e.g., in terms of program output, execution time, or (DNN) classification. The key challenge of differential software analysis is to simultaneously reason about multiple program paths, often across program variants.

BibTeX
@inproceedings{Noller-al:ICSE20,
  author    = {Yannic Noller and
               Corina S. Pasareanu and
               Marcel B{\"{o}}hme and
               Youcheng Sun and
               Hoang Lam Nguyen and
               Lars Grunske},
  title     = {{HyDiff:} hybrid differential software analysis},
  booktitle = {ICSE},
  pages     = {1273--1285},
  publisher = {{ACM}},
  year      = {2020},
}

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